As artificial intelligence (AI) continues to permeate an increasing portion of daily business life, the need for robust cybersecurity measures has become vital.

AI systems, with their complex algorithms and vast datasets, present unique challenges for traditional security measures. AI systems are vulnerable to a range of cyberthreats, including prompt injection, evasion attacks, training data poisoning, model denial of service, and model theft:

• Prompt injection involves manipulating the input provided to an AI model to elicit unintended or malicious responses, potentially leading to biased, inaccurate, or harmful outputs.
• Evasion attacks involve modifying input data in subtle ways that are imperceptible to humans but can cause AI systems to misclassify or make incorrect decisions.
• Training data poisoning involves introducing malicious data into the datasets used to train AI models, leading to compromised systems that may produce biased or harmful outputs.
• Model denial-of-service attacks aim to overwhelm AI systems with excessive requests or complex inputs, rendering them unresponsive or significantly degrading their performance.
• Model theft involves unauthorised access and extraction of AI models, leading to intellectual property theft, unauthorised use, and potential security risks.

Adopting The Right Strategy

To effectively safeguard AI systems, organisations must adopt a comprehensive cybersecurity strategy. This includes implementing AI security standards, controlling access to AI models and securing the code.

Security teams should also consult with external security experts, encrypt model data, monitor for anomalies, and train staff on AI security.

AI security standards, such as ISO/IEC 27001, provide a framework for developing and maintaining secure AI systems. Controlling access to AI models involves restricting access to authorised personnel using robust authentication and authorisation mechanisms.

Securing the code underlying AI systems requires conducting regular code reviews, vulnerability assessments, and following secure coding practices.

At the same time, encrypting model data ensures that sensitive information is protected from unauthorised access. Monitoring AI systems for unusual behaviour or performance indicators can also help detect potential security breaches.

Areas of Particular Concern

Certain types of AI systems, such as large-language models, autonomous vehicles, financial AI models, and healthcare AI systems, require particular attention due to their critical functions or the sensitivity of the data they handle.

These systems may be more vulnerable to specific threats and require tailored security measures. For example, large-language models may be susceptible to prompt injection attacks, while autonomous vehicles may be targeted by evasion attacks that could lead to accidents.

As AI technology continues to advance, so too will the threats faced by these systems. Organisations must stay ahead of the curve by investing in ongoing research and development of AI security solutions. Collaboration between industry, academia, and governments is also essential to address the global challenges and opportunities presented by AI cybersecurity.

By implementing robust security measures and staying informed about emerging threats, organisations can protect their AI investments and ensure the ethical and responsible development of this transformative technology.

Additional Factors to Consider

As organisations come to terms with the increasing relationship between AI and cybersecurity, there are a range of other factors that will need to be considered. These include:

• Continuous monitoring and improvement: AI security is an ongoing process, and so organisations must regularly assess their security posture and make necessary improvements to stay ahead of evolving threats.
• Regulatory compliance: AI systems may be subject to various regulations, such as data protection laws or industry-specific standards. Organisations must therefore comply with applicable regulations to avoid legal and reputational risks.
• Supply-chain security: AI systems often rely on third-party components and services. Organisations must ensure the security of their supply chain to mitigate risks associated with vulnerabilities in third-party components.
• Emerging threats: As AI technology evolves, new threats may emerge. Organisations must stay informed about trends and be prepared to adapt their security measures accordingly.
• AI governance: Establishing effective AI governance frameworks can help organisations manage AI risks and ensure that AI systems are developed and used responsibly.
• Human oversight: While AI can automate many security tasks, human oversight remains crucial. Security professionals should be involved in decision-making and monitoring AI systems for anomalies.

By carefully considering these additional factors, organisations can further strengthen their AI cybersecurity posture and ensure the long-term success of their AI initiatives.

The evolution of the technology is showing no sign of slowing. Those organisations that position themselves best to keep up with the pace will enjoy significant future business benefits.